---
# System prepended metadata

title: Retail  Sector Reasoning questions

---

# Retail  Sector Reasoning questions

This document is about the different kind of questions that can be expected from the customers of Knowledge graph from Retail Sector.

Different entities involved in the retail sector:

1. Catalog
2. Merchant_master
3. jio_mart_transaction

### Schema of the entities:

#### 1. Catalog:
 This contains attributes realated to the products sold on platform. The attributes are like productskuid, productid, productname, brandname, l1categoryname, l2categoryname, l3categoryname.

| productskuid | productid | productname | brandname | l1categoryname | l2categoryname | l3categoryname  |
|-|-|-|-|-|-|-|
| 10000011 | 100000011 | Septona MediCare Underpads 15 pcs | Septona | Beauty & Hygiene | Health & Wellness | Adult Diapers  |
| 10000015 | 100000013 | Kare In Adult Diapers (M) 10 count | Kare In | Beauty & Hygiene | Health & Wellness | Adult Diapers  |
| 10000266 | 100000188 | Old Spice Sport After Shave Lotion Atomiser 100 ml | Old Spice | Beauty & Hygiene | Shaving Needs | After Shave  |

#### 2. Merchant_master:
 In this, we can find all attributes related to stores such as storeid, merchant_name (store name), storepincode, merchant_id (store id), lattitude, longitude, jio_point_name, jc_name, city_name, extsecondarystoreid.

| storeid | merchant_name | storepincode | merchant_id | lattitude | longitude | jio\_point\_name | jc_name | city_name | extsecondarystoreid  |
|-|-|-|-|-|-|-|-|-|-|
| 1001008 | sri ganesh store | 628502 | 100001001577533 | 13.15553 | 80.225267 | Kiburn Nagar | Madhavaram, Chennai | Chennai |   |
| 1001344 | lakshmi provision store | 600118 | 100001001578517 | 13.135013 | 80.257343 | Erukkancheri | Madhavaram, Chennai | Chennai |   |
| 1007221 | namma mini mart | 560043 | 100001001645325 | 13.038298 | 77.667938 | Essel Gardens | Banaswadi, Bangalore | Bengaluru |   |


#### 3. jio_mart_transaction: 
This entity consists of attribuutes related to transactions made at a store like purchaseid, productskuid, totalamount, quantity, storeid, invoicenumber, invoicedate, invoiceamount.

| purchaseid | productskuid | totalamount | quantity | storeid | invoicenumber | invoicedate | invoiceamount  |
|-|-|-|-|-|-|-|-|
| 100661527 | 10051418 | 54 | 1 | 1179243 | P202100000005 | 02-01-2021 | 1560.87  |
| 100661527 | 10048990 | 208.35 | 5 | 1179243 | P202100000005 | 02-01-2021 | 1560.87  |
| 100661527 | 10117315 | 348 | 3 | 1179243 | P202100000005 | 02-01-2021 | 1560.87  |
| 100661527 | 10104513 | 627.84 | 144 | 1179243 | P202100000005 | 02-01-2021 | 1560.87  |
| 100661527 | 10104514 | 213.6 | 24 | 1179243 | P202100000005 | 02-01-2021 | 1560.87  |
| 100661527 | 10008820 | 109.08 | 12 | 1179243 | P202100000005 | 02-01-2021 | 1560.87  |

### Predicates:

1. 

### Questions (Attribute Based):

All the attribute based questions which are present in schema can be answered by knowledge graph.

Examples:

1.  What is level of the product with id (assume product id here)
2.  Given the name of the distribution center, what is the zone of the DC?

In the same way, questions regarding attributes related to entities like Brand, Person, store, address etc can be answered.
